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Department: Computer Sotware and Inormation Technology Engineering CSE IT

Computational Intelligence - IT8601

Online Study Material, Lecturing Notes, Assignment, Reference, Wiki and important questions and answers

Computational Intelligence
-:- What is Artificial Intelligence?
-:- History of Artificial Intelligence(AI)
-:- Components of Artificial Intelligence(AI)
-:- Definition of Artificial intelligence
-:- Weak And Strong Artificial Intelligence(AI)
-:- Areas and Task Domain of Artificial Intelligence(AI)
-:- Artificial Intelligence(AI) Technique
-:- 7- Problem Characteristics
-:- Production System: Characteristics, Features , Disadvantages
-:- Algorithm of Problem Solving
-:- Various Types of Artificial Intelligence Problems and their Solutions
-:- Searching Algorithms - Artificial Intelligence(AI)
-:- Uninformed Search
-:- Breadth First Search (BFS): Concept, Implementation, Advantages, Disadvantages
-:- Depth First Search (DFS): Concept, Implementation, Advantages, Disadvantages
-:- Brute Force or Blind Search(BFS)
-:- Greedy Search
-:- Informed Search (Heuristic Search)
-:- Best First Search: Concept, Algorithm, Implementation, Advantages, Disadvantages
-:- Branch and Bound Search: Concept, Algorithm, Implementation, Advantages, Disadvantages
-:- A* Search: Concept, Algorithm, Implementation, Advantages, Disadvantages
-:- AO* Search(Graph): Concept, Algorithm, Implementation, Advantages, Disadvantages
-:- Hill Climbing Search Algorithm: Concept, Algorithm, Advantages, Disadvantages
-:- Knowledge Based Systems
-:- Type of Knowledge
-:- Knowledge Acquisition Techniques
-:- Knowledge representation
-:- Properties for knowledge Representation
-:- Syntax and semantics for Knowledge Representation
-:- Types of Knowledge Representation
-:- Human Associative Memory (HAM)
-:- MIN-MAX Search Algorithm
-:- Constraint Satisfaction Search Algorithm
-:- Planning
-:- Basic Components of a Planning System
-:- Planning in State Space Search
-:- Various Planning Techniques
-:- Understanding in Artificial Intelligence
-:- Natural Language Processing
-:- Goals, Applications and Examples of Natural Language Processing(NLP)
-:- ELIZA, HAL, LUNAR, SHRDLU System
-:- The Chomsky Hierarchy of Grammars
-:- Transformational Grammar
-:- Case Grammars (FILLMORE’s Grammar)
-:- Semantic Grammars
-:- Context Free Grammar (CFG)
-:- Parsing Process
-:- Types of Parsing
-:- Transition Network
-:- Types of Transition Network
-:- Learning - Artificial Intelligence
-:- Classification of Learning
-:- Multi Agent Learning
-:- Explanation based Learning
-:- Genetic Algorithm
-:- Applications and Advantages of Genetic Algorithm
-:- Neural Network
-:- Features Of Artificial Network (ANN)
-:- Types of Neural Networks
-:- Feed Forward neural network
-:- Back Propagation neural network
-:- Clustering and Types of Clustering
-:- Pattern Recognition
-:- Expert System
-:- Difference Between Expert System and Conventional System
-:- The Development Process of an Expert System
-:- Characteristics of an Expert System
-:- Structure of an Expert System
-:- Rule Based Architecture of an Expert System
-:- Applications of Expert Sytem
-:- Artificial Intelligence(AI) Problem Solving
-:- Intelligent Agent
-:- Uninformed Search Strategies
-:- Heuristics / Informed Search Strategies
-:- Constraint Satisfaction Problems (CSPS)
-:- Important Questions and Answers: Algorithm of Problem Solving
-:- Logical Agents
-:- Important Questions and Answers: Logical Agents
-:- Planning With State Space Search
-:- Partial Order Planning
-:- Important Questions and Answers: Artificial Intelligence Planning
-:- Uncertainty - Artificial Intelligence
-:- Important Questions and Answers: Artificial Intelligence - Uncertainty
-:- Learning from observation
-:- Inductive Learning
-:- Learning Decision Trees
-:- Explanation Based Learning
-:- Statistical Learning Methods
-:- Reinforcement Learning
-:- Important Questions and Answers: Artificial Intelligence Learning
-:- What is Artificial Intelligence(AI)?
-:- Importance of Artificial Intelligence(AI)
-:- Early work in Artificial Intelligence(AI)
-:- Artificial Intelligence(AI) and related fields
-:- Search and Control Strategies
-:- Examples of search problems
-:- Uniformed or Blind search
-:- Informed search
-:- Constraint Satisfaction Search
-:- Heuristic Repair
-:- Tabu Search
-:- Simulated Annealing
-:- Real-Time A*
-:- Propositional and Predicate Logic
-:- Semantics
-:- Predicate Calculus
-:- First-Order Predicate Logic(FOPL)
-:- Modal Logics and Possible Worlds
-:- Dempster- Shafer theory
-:- Probabilistic Reasoning
-:- Definition and importance of knowledge
-:- Representation of knowledge
-:- Knowledge Organization and Manipulation
-:- Matching techniques
-:- Structures used in Matching
-:- Measure for Matching
-:- Matching like Patterns
-:- Partial Matching
-:- The RETE matching algorithm
-:- Knowledge Organization and Management
-:- Indexing and retrieval techniques
-:- Integrating knowledge and memory
-:- Natural Language Processing
-:- Artificial Intelligence: Grammars and Languages
-:- Basic parsing techniques
-:- Augmented Transition Networks
-:- Chart Parsing
-:- Semantic Analysis
-:- Expert System Architecture
-:- Rules for Knowledge Representation
-:- Rule-Based Systems
-:- Rule-Based Expert Systems
-:- Architecture of an Expert System
-:- Knowledge Engineering
-:- CLIPS (C Language Integrated Production System)
-:- Backward Chaining in Rule-Based Expert Systems
-:- CYC
-:- What is Artificial intelligence(AI)?
-:- Production System
-:- Game playing
-:- Iterative Deepening
-:- Knowledge Representation
-:- Predicate Calculus
-:- Predicate Logic
-:- AI Resolution: Definition and Principle
-:- Structured Represntation of Knowledge
-:- Knowledge Representation
-:- Framework of Knowledge Representation (Poole 1998)
-:- Knowledge Representation Schemes
-:- Issues in Knowledge Representation
-:- KR Using Predicate Logic
-:- KR Using Rules
-:- Planning and Machine Learning
-:- Symbolic Reasoning
-:- Statistical Reasoning
-:- Probability and Bayes’ Theorem
-:- Certainty Factors in Rule-Based Systems
-:- Bayesian Networks and Certainty Factors
-:- Dempster - Shafer Theory (DST)
-:- Fuzzy Logic
-:- Expert System
-:- Expert System Characteristics
-:- Expert System Features
-:- Knowledge Acquisition
-:- Knowledge Base (Representing and Using Domain Knowledge)
-:- Inference Engine
-:- Expert System Shells
-:- Explanation - Expert System
-:- Application of Expert Systems